Information-Based Nonlinear Approximation: An Average Case Setting

Plaskota, Leszek

doi:10.4230/DagSemProc.04401.5

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Information-Based Nonlinear Approximation: An Average Case Setting

Author Leszek Plaskota

Part of: Volume: Dagstuhl Seminar Proceedings, Volume 4401
Part of: Series: Dagstuhl Seminar Proceedings (DagSemProc)
License: Creative Commons Attribution 4.0 International license
Publication Date: 2005-04-19

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DOI: 10.4230/DagSemProc.04401.5
URN: urn:nbn:de:0030-drops-1504

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Keywords

average case setting
nonlinear approximation
information-based comlexity

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Abstract

Nonlinear approximation has usually been studied
under deterministic assumption and complete
information about the underlying functions. 
We assume  only partial information and we are 
interested in the average case error and 
complexity of approximation. It turns out that 
the problem can be essentially split into two 
independent problems related to average case 
nonlinear (restricted) approximation from 
complete information, and average case 
unrestricted approximation from partial 
information. The results are then applied to 
average case piecewise polynomial approximation, 
and to average case approximation of real 
sequences.

Cite As Get BibTex

Leszek Plaskota. Information-Based Nonlinear Approximation: An Average Case Setting. In Algorithms and Complexity for Continuous Problems. Dagstuhl Seminar Proceedings, Volume 4401, p. 1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2005) https://doi.org/10.4230/DagSemProc.04401.5

Author Details

Leszek Plaskota

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